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AI role · Open offer

LLM Engineer — legal document AI

Luminance

Full-time
Senior
Legal AI
Hybrid
30 Old Bailey, London, England EC4M 7AU, United Kingdom · Hybrid
London, United Kingdom
£95k–£130k + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: 30 Old Bailey, London, England EC4M 7AU, United Kingdom · Hybrid
Compensation: £95k–£130k + equity
Experience level: Senior
Team: Legal AI
Benefits & perks: Equity, private medical, hybrid.
How to apply: RAG or contract-review workflow with citation requirements.
Company site: www.luminance.com
Listed: Jun 7, 2026
Updated: Aug 3, 2026

About this role

What the team is looking for and how this position fits.

Luminance is hiring a Senior LLM Engineer — legal document AI on the Legal AI team (Hybrid · London, United Kingdom). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Luminance applies AI to contract review, due diligence, and compliance for global law firms and in-house legal teams.

LLM Engineer (London): RAG over privileged documents, citation fidelity, and workflow integrations with iManage / NetDocuments.

You will balance model quality with auditability—every answer must be traceable to source clauses.

What you will do

Build retrieval pipelines with chunking strategies tuned for legal PDFs and tables.
Implement guardrails for privilege, jurisdiction, and hallucination thresholds.
Run offline evals with attorney-reviewed golden sets before beta releases.
Collaborate with customer success on EU/UK data residency requirements.

Stack & environment

Python, LangChain, vector databases, OpenAI / Anthropic APIs.
Dockerised services, hybrid London (Old Bailey area).

What we look for

Production RAG with citation or grounding requirements.
Comfort discussing precision/recall trade-offs with non-ML stakeholders.
Bonus: NLP on long documents or multilingual corpora.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
OpenAI API
Calling frontier APIs with retries, cost controls, prompt versioning, and safety filters.
Vector DBs
Embedding storage, hybrid search, and freshness strategies for RAG and semantic retrieval.

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: RAG or contract-review workflow with citation requirements.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, Vector DBs, and Docker.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · London, United Kingdom)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: £95k–£130k + equity, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

RAG or contract-review workflow with citation requirements.

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LLM Engineer — legal document AI

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